Naive Bayes classification of MNIST images http://web.iitd.ac.in/~bspanda/BY.pdf

naiveBayes.py

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import scipy.stats

digits = datasets.load_digits()
trainX, testX, trainY, testY = train_test_split(digits.images, digits.target, test_size=.2)

Y_freq = {y: sum(trainY==y) for y in range(10)}
X_stats = {y: {"X_mean": np.mean(trainX[trainY==y], axis=0), \
               "X_std": np.std(trainX[trainY==y], axis=0) \
              } \
           for y in range(10)
          }


P = np.zeros((testX.shape[0], 10))
for row, x in enumerate(testX):
    probs = []
    for y in range(10):
        mean, std = X_stats[y]["X_mean"], X_stats[y]["X_std"]
        A = scipy.stats.norm(mean, std).pdf(x)
        B = np.where(np.all([x==mean, std==0], axis=0), 1, A)
        C = np.where(np.all([x!=mean, std==0], axis=0), 1e-4, B)
        probs.append(np.product(C)*Y_freq[y])
    P[row] = probs

pred = np.argmax(P, axis=1)

acc = np.sum(pred==testY)/testY.shape[0]

print(acc)

Note: Acc obtained is 88.88%